Beyond Trophic Factors: Exploiting the Intrinsic Regenerative Properties of Adult Neurons
Bibliographic record
Abstract
Injuries and diseases of the peripheral nervous system (PNS) are common but frequently irreversible. It is often but mistakenly assumed that peripheral neuron regeneration is robust without a need to be improved or supported. However, axonal lesions, especially those involving proximal nerves rarely recover fully and injuries generally are complicated by slow and incomplete regeneration. Strategies to enhance the intrinsic growth properties of reluctant adult neurons offer an alternative approach to consider during regeneration. Since axons rarely regrow without an intimately partnered Schwann cell (SC), approaches to enhance SC plasticity carry along benefits to their axon partners. Direct targeting of molecules that inhibit growth cone plasticity can inform important regenerative strategies. A newer approach, a focus of our laboratory, exploits tumor suppressor molecules that normally dampen unconstrained growth. However several are also prominently expressed in stable adult neurons. During regeneration their ongoing expression "brakes" growth, whereas their inhibition and knockdown may enhance regrowth. Examples have included phosphatase and tensin homolog deleted on chromosome ten (PTEN), a tumor suppressor that inhibits PI3K/pAkt signaling, Rb1, the protein involved in retinoblastoma development, and adenomatous polyposis coli (APC), a tumor suppressor that inhibits β-Catenin transcriptional signaling and its translocation to the nucleus. The identification of several new targets to manipulate the plasticity of regenerating adult peripheral neurons is exciting. How they fit with canonical regeneration strategies and their feasibility require additional work. Newer forms of nonviral siRNA delivery may be approaches for molecular manipulation to improve regeneration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".